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@Article{BispoPPKBRSRTSA:2019:MaFoSu,
               author = "Bispo, Polyanna da Concei{\c{c}}{\~a}o and Pardini, Matteo and 
                         Papathanassiou, Konstantinos P. and Kluger, Florian and Balzter, 
                         Heiko and Rains, Dominik and Santos, Jo{\~a}o Roberto dos and 
                         Rizaev, Igor G. and Tansey, Kevin and Santos, Maiza Nara dos and 
                         Araujo, Luciana Spinelli",
          affiliation = "{University of Leicester} and {German Aerospace Center (DLR)} and 
                         {German Aerospace Center (DLR)} and {German Aerospace Center 
                         (DLR)} and {University of Leicester} and {Ghent University} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {University 
                         of Bristol} and {University of Leicester} and {Empresa Brasileira 
                         de Pesquisa Agropecu{\'a}ria (EMBRAPA)} and {Empresa Brasileira 
                         de Pesquisa Agropecu{\'a}ria (EMBRAPA)}",
                title = "Mapping forest successional stages in the Brazilian Amazon using 
                         forest heights derived from TanDEM-X SAR interferometry",
              journal = "Remote Sensing of Environment",
                 year = "2019",
               volume = "232",
                pages = "111194",
                month = "Oct.",
             keywords = "Tropical forests, Successional stages, Forest height, Synthetic 
                         Aperture Radar, Interferometry, TanDEM-X.",
             abstract = "Knowledge of the spatial patterns of successional stages (i.e., 
                         primary and secondary forest) in tropical forests allows to 
                         monitor forest preservation, mortality and regeneration in 
                         relation to natural and anthropogenic disturbances. Different 
                         successional stages have also different capabilities of 
                         re-establishing carbon stocks. Therefore, a successful 
                         discrimination of successional stages over wide areas can lead to 
                         an improved quantification of above ground biomass and carbon 
                         stocks. The reduction of the mapping uncertainties is especially a 
                         challenge due to high heterogeneity of the tropical vegetation. In 
                         this framework, the development of innovative remote sensing 
                         approaches is required. Forests (top) height (and its spatial 
                         distribution) are an important structural parameter that can be 
                         used to differentiate between different successional stages, and 
                         can be provided by Interferometric Synthetic Aperture Radar 
                         (InSAR) acquisitions. In this context, this paper investigates the 
                         potential of forest heights estimated from TanDEM-X InSAR data and 
                         a LiDAR digital terrain model (DTM) for separating successional 
                         stages (primary or old growth and secondary forest at different 
                         stages of succession) by means of a maximum likelihood 
                         classification. The study was carried out in the region of the 
                         Tapaj{\'o}s National Forest (Par{\'a}, Brazil) in the Amazon 
                         biome. The forest heights for three years (2012, 2013 and 2016) 
                         were estimated from a single-polarization in bistatic mode using 
                         InSAR model-based inversion techniques aided by the LiDAR digital 
                         terrain model. The validation of the TanDEM-X forest heights with 
                         independent LiDAR H100 datasets was carried out in the location of 
                         seven field inventory plots (measuring 50  50 m, equivalent to 
                         0.25 ha), also allowing for the validation of the LiDAR datasets 
                         against the field data. The validation of the estimated heights 
                         showed a high correlation (r = 0.93) and a low uncertainty (RMSE = 
                         3 m). The information about the successional stages and forest 
                         heights from field datasets was used to select training samples in 
                         the LiDAR and TanDEM-X forest heights to classify successional 
                         stages with a maximum likelihood classifier. The identification of 
                         different stages of forest succession based on TanDEM-X forest 
                         heights was possible with an overall accuracy of about 80%.",
                  doi = "10.1016/j.rse.2019.05.013",
                  url = "http://dx.doi.org/10.1016/j.rse.2019.05.013",
                 issn = "0034-4257",
             language = "en",
           targetfile = "bispo_mapping.pdf",
        urlaccessdate = "22 abr. 2021"
}


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